In this study, we investigate the effects of the weight function in the bounded influence regression quantile (BIRQ) estimator for the AR(l) model with additive outliers. In order to down-weight the outliers of X -axis, the Mallows' (1973) weight function has been commonly used in the BIRQ estimator. However, in our Monte Carlo study, the BIRQ estimator using the Tukey's bisquare weight function shows less MSE and bias than that of using the Mallows' weight function or Huber's weight function. Thus, the use of the Tukey's weight function is recommended in the BIRQ estimator for our model.

Keywords

Weight Function;AR(1);Regression quantile estimator;

Language

Korean

Cited by

References

1.

한상문, 정병철 (2004). AR(1) 모형의 모수에 대한 L-추정법, '응용통계연구' Accepted

2.

De Jongh, P.J. and De Wet T. (1985). Trimmed Mean and Bounded Influence Estimators for the Parameters of the AR(1) Process, Communications in Statistics -Theory and Methods, Vol. 14, 1361-1375